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Pedestrian navigation systems (PNS) using foot-mounted MEMS inertial sensors use zero-velocity updates (ZUPTs) to reduce drift in navigation solutions and estimate inertial sensor errors. However, it is well known that ZUPTs cannot reduce all errors, especially as heading error is not observable. Hence, the position estimates tend to drift and even cyclic ZUPTs are applied in updated steps of the Extended Kalman Filter (EKF). This urges the use of other motion constraints for pedestrian gait and any other valuable heading reduction information that is available. In this paper, we exploit two more motion constraints scenarios of pedestrian gait: (1) walking along straight paths; (2) standing still for a long time. It is observed that these motion constraints (called “virtual sensor”), though considerably reducing drift in PNS, still need an absolute heading reference. One common absolute heading estimation sensor is the magnetometer, which senses the Earth’s magnetic field and, hence, the true heading angle can be calculated. However, magnetometers are susceptible to magnetic distortions, especially in indoor environments. In this work, an algorithm, called magnetic anomaly detection (MAD) and compensation is designed by incorporating only healthy magnetometer data in the EKF updating step, to reduce drift in zero-velocity updated INS. Experiments are conducted in GPS-denied and magnetically distorted environments to validate the proposed algorithms.

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When a linear actuator is used for rotation motion by a knee joint of an exoskeleton, the specifications of the joint range of motion (ROM) and joint torque change according to how the linear actuator are attached. Moreover, while the linear actuator generates a constant amount of force, the joint torque generated by the actuator changes according to the joint angle, which causes the torque contraction. This makes it difficult to meet the required torque and ROM for walk and stand-to-sit and sit-to-stand (STS) motions while carrying a load. To solve these problems we propose a novel knee joint for an exoskeleton with good energy efficiency during walk and STS motions while carrying a load. The mechanism is composed of a four-bar linkage and an elastic element. Based on an analysis of human motion, the design variables of the joint were optimized and the feasibility of the optimized variables was verified through the simulation. The findings from the simulation results suggest that combining a four-bar linkage with a linear actuator allows a large ROM and good torque performance of the knee joint for walk and STS motions. Moreover, the energy efficiency can be improved because the spring mounted parallel to the actuator can store the energy dissipated as negative work and recycle the energy as positive work.